locality characteristics of web streams revisited (spects 2005)

8/6/2019 Locality Characteristics of Web Streams Revisited (SPECTS 2005)

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Locality Characteristics

of Web Streams Revisited

Aniket Mahanti, Anirban Mahanti, andCarey Williamson

University of Calgary, Canada

International Symposium on Performance Evaluation of Computer and

Telecommunication Systems, Philadelphia, 2005


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Introduction

Motivation

ADF framework

Objectives


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3

Introduction

Web caching proxies are an effectivemeans of reducing network traffic

Web caches are widely deployed by ISPs

Caches improve performance byexploiting workload characteristicssuch as locality of reference

Workload characterisation of localitystructure can provide insight into thedesign and performance of the Web


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Motivation

Locality characteristics can be used bycaching policies when making decisionsto evict or retain documents in the cache

Most prior Web caching work focused onanalysing Web streams in isolation

[Fonseca et al. 2005] proposed a

system level view called the ADFframework for analysing transformationsof a Web reference stream


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ADFFramework [Fonseca, 2005]

Reference: R. Fonseca et al. (2005), Locality in a Web of Streams, In: Communications of the ACM, 48(1):8288.

A:AggregationD:Disaggregation

F:Filtering


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Research Objectives

Study locality properties in Web request

streams using the ADF framework:

What impact do locality characteristicshave on caching performance?

What are the locality characteristics ofWeb request streams after the

aggregation of filtered streams?


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Background

Flow of requests

Locality of reference


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Flow of Requests

Image reproduced from: R. Fonseca et al. (2003), Locality in a Web of Streams, Technical Report, Department of Computer Science, Boston University.


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Locality of Reference

Popularity: An object is simply more

popular than other objects

.XABXXCXDXXXEFXX.

Temporal locality: References to an

object occur in a correlated manner.AAHIJAAAUOLYPJKAA.


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Metrics Used


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Performance Metrics

Document hit ratio: Percentage of totalrequests satisfied by Web proxy cache

Byte hit ratio: Percentage of total byte volumeof data satisfied by Web proxy cache

Cumulative reference measure: Fraction oftotal requests accounted for by the top 10%of the most popular documents

Inter-reference measure: Probability ofreferencing document again within Mintervening requests (e.g., M=1000)


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FilteringModel

Model description

Simulation results


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FilteringModel

Filtering

Input stream

Filtered stream (misses)


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Workload and System Parameters

WebTraff: synthetic Web proxy workloads Two traces differing only in temporal locality

Trace1 (weak) and Trace 2 (strong)

Trace characteristics:

1.5 million requests

495,000 unique documents

14 GB total bytes of Web content

Cache replacement policies:LRU, LFU-Aging, GDS, RAND, FIFO

Cache size:

1 MB 16 GB


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Caching Performance (1 of 3)

Trace1: Weak temporal locality

Document Hit Ratio


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Trace2: Strong temporal locality

Document Hit Ratio


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Trace1: Weak temporal locality Trace2: Strong temporal locality

Document Hit Ratio


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Popularity Characteristics


Cumulative Reference Measure

forFiltered Request Stream

(after the cache)


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Temporal Locality Characteristics


Inter-referenceMeasure

forFiltered Request Stream

(after the cache)


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Aggregation Model

Model description

Simulation results


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Aggregation Model

Aggregated

filtered stream

Filtering

Input stream

Filtered stream


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System Model and Parameters

Two-level hierarchal web proxy configuration

Aggregated streams from:N = 1, 2, 4, 8 child proxies

Caching policy:LRU at child proxies

Cache size:

1 MB 256 MB Degree of overlap:No overlap, partial overlap


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Popularity Characteristics

No Overlap Partial Overlap

Cumulative Reference Measure

(Strong temporal locality)


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Temporal Locality Characteristics

No Overlap Partial Overlap

Inter-referenceMeasure

(Strong temporal locality)


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No Overlap: Temporal Locality

Temporal locality decreases with increasingN Phenomenon consistent over various cache

sizes and degree of temporal locality

Design of the no overlap scenario

New stream has twice as many documentsbetween 1A1 and

2A1

N=2

Child Proxy 1: 1A1,1U1 ,

1U2,.,1U50,

1A1

Child Proxy 2: 2A1,2U1 ,

2U2 ,.,2U50,

2A1

Aggregated filtered stream:1

A1,2

A1,1

U1,2

U1,,1

U50,2

U50,1

A1,2

A1,


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Partial Overlap: Temporal Locality

Temporal locality increases with increasingN Due to 50% overlap among all traces

References ofA from other proxies clustered

N=4

Child Proxy 1:A, B,1U1 ,.,1U50,A, B




Aggregated filtered stream:A, A, A, A, B, B, B, B,1U1 ,2U1 ,

3U1 ,4U1

,.,1U50,2U50,

3U50 ,4U50,A, A, A, A, B, B, B, B

,


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Conclusions


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Conclusions

Caching policies should exploit temporalcorrelation andpopularityof documents

LRU and FIFO exploit temporal locality

GDS insensitive to changes in temporal locality Structural change in temporal locality for

aggregated streams depends on the degree

of overlap in the workloads

These results imply limited advantages of

using caching hierarchies

locality characteristics of web streams revisited (spects 2005)

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